Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations4269
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory433.7 KiB
Average record size in memory104.0 B

Variable types

Numeric10
Categorical3

Alerts

bank_asset_value is highly overall correlated with commercial_assets_value and 4 other fieldsHigh correlation
cibil_score is highly overall correlated with loan_statusHigh correlation
commercial_assets_value is highly overall correlated with bank_asset_value and 3 other fieldsHigh correlation
income_annum is highly overall correlated with bank_asset_value and 4 other fieldsHigh correlation
loan_amount is highly overall correlated with bank_asset_value and 4 other fieldsHigh correlation
loan_status is highly overall correlated with cibil_scoreHigh correlation
luxury_assets_value is highly overall correlated with bank_asset_value and 4 other fieldsHigh correlation
residential_assets_value is highly overall correlated with bank_asset_value and 3 other fieldsHigh correlation
loan_id is uniformly distributedUniform
loan_id has unique valuesUnique
no_of_dependents has 712 (16.7%) zerosZeros
residential_assets_value has 45 (1.1%) zerosZeros
commercial_assets_value has 107 (2.5%) zerosZeros

Reproduction

Analysis started2025-09-25 03:17:15.516855
Analysis finished2025-09-25 03:17:32.161440
Duration16.64 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

loan_id
Real number (ℝ)

Uniform  Unique 

Distinct4269
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2135
Minimum1
Maximum4269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2025-09-25T03:17:32.347658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile214.4
Q11068
median2135
Q33202
95-th percentile4055.6
Maximum4269
Range4268
Interquartile range (IQR)2134

Descriptive statistics

Standard deviation1232.4985
Coefficient of variation (CV)0.57728266
Kurtosis-1.2
Mean2135
Median Absolute Deviation (MAD)1067
Skewness0
Sum9114315
Variance1519052.5
MonotonicityStrictly increasing
2025-09-25T03:17:32.577592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42691
 
< 0.1%
11
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
Other values (4259)4259
99.8%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
42691
< 0.1%
42681
< 0.1%
42671
< 0.1%
42661
< 0.1%
42651
< 0.1%
42641
< 0.1%
42631
< 0.1%
42621
< 0.1%
42611
< 0.1%
42601
< 0.1%

no_of_dependents
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4987116
Minimum0
Maximum5
Zeros712
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2025-09-25T03:17:32.741498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6959102
Coefficient of variation (CV)0.67871383
Kurtosis-1.2569922
Mean2.4987116
Median Absolute Deviation (MAD)1
Skewness-0.017970543
Sum10667
Variance2.8761113
MonotonicityNot monotonic
2025-09-25T03:17:32.888134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4752
17.6%
3727
17.0%
0712
16.7%
2708
16.6%
1697
16.3%
5673
15.8%
ValueCountFrequency (%)
0712
16.7%
1697
16.3%
2708
16.6%
3727
17.0%
4752
17.6%
5673
15.8%
ValueCountFrequency (%)
5673
15.8%
4752
17.6%
3727
17.0%
2708
16.6%
1697
16.3%
0712
16.7%

education
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.5 KiB
Graduate
2144 
Not Graduate
2125 

Length

Max length13
Median length9
Mean length10.991099
Min length9

Characters and Unicode

Total characters46921
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduate
2nd rowNot Graduate
3rd rowGraduate
4th rowGraduate
5th rowNot Graduate

Common Values

ValueCountFrequency (%)
Graduate2144
50.2%
Not Graduate2125
49.8%

Length

2025-09-25T03:17:33.070516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T03:17:33.214625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
graduate4269
66.8%
not2125
33.2%

Most occurring characters

ValueCountFrequency (%)
a8538
18.2%
6394
13.6%
t6394
13.6%
G4269
9.1%
d4269
9.1%
r4269
9.1%
u4269
9.1%
e4269
9.1%
N2125
 
4.5%
o2125
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)46921
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a8538
18.2%
6394
13.6%
t6394
13.6%
G4269
9.1%
d4269
9.1%
r4269
9.1%
u4269
9.1%
e4269
9.1%
N2125
 
4.5%
o2125
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)46921
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a8538
18.2%
6394
13.6%
t6394
13.6%
G4269
9.1%
d4269
9.1%
r4269
9.1%
u4269
9.1%
e4269
9.1%
N2125
 
4.5%
o2125
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)46921
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a8538
18.2%
6394
13.6%
t6394
13.6%
G4269
9.1%
d4269
9.1%
r4269
9.1%
u4269
9.1%
e4269
9.1%
N2125
 
4.5%
o2125
 
4.5%

self_employed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.5 KiB
Yes
2150 
No
2119 

Length

Max length4
Median length4
Mean length3.5036308
Min length3

Characters and Unicode

Total characters14957
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
Yes2150
50.4%
No2119
49.6%

Length

2025-09-25T03:17:33.580453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T03:17:33.651702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes2150
50.4%
no2119
49.6%

Most occurring characters

ValueCountFrequency (%)
4269
28.5%
Y2150
14.4%
e2150
14.4%
s2150
14.4%
N2119
14.2%
o2119
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)14957
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4269
28.5%
Y2150
14.4%
e2150
14.4%
s2150
14.4%
N2119
14.2%
o2119
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14957
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4269
28.5%
Y2150
14.4%
e2150
14.4%
s2150
14.4%
N2119
14.2%
o2119
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14957
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4269
28.5%
Y2150
14.4%
e2150
14.4%
s2150
14.4%
N2119
14.2%
o2119
14.2%

income_annum
Real number (ℝ)

High correlation 

Distinct98
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5059123.9
Minimum200000
Maximum9900000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2025-09-25T03:17:33.761996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum200000
5-th percentile600000
Q12700000
median5100000
Q37500000
95-th percentile9400000
Maximum9900000
Range9700000
Interquartile range (IQR)4800000

Descriptive statistics

Standard deviation2806839.8
Coefficient of variation (CV)0.55480749
Kurtosis-1.182729
Mean5059123.9
Median Absolute Deviation (MAD)2400000
Skewness-0.012814425
Sum2.15974 × 1010
Variance7.8783498 × 1012
MonotonicityNot monotonic
2025-09-25T03:17:33.914277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700000062
 
1.5%
410000059
 
1.4%
760000057
 
1.3%
470000056
 
1.3%
530000055
 
1.3%
690000055
 
1.3%
320000055
 
1.3%
390000054
 
1.3%
800000053
 
1.2%
900000053
 
1.2%
Other values (88)3710
86.9%
ValueCountFrequency (%)
20000042
1.0%
30000051
1.2%
40000035
0.8%
50000046
1.1%
60000049
1.1%
70000045
1.1%
80000041
1.0%
90000039
0.9%
100000042
1.0%
110000045
1.1%
ValueCountFrequency (%)
990000035
0.8%
980000048
1.1%
970000040
0.9%
960000039
0.9%
950000040
0.9%
940000047
1.1%
930000033
0.8%
920000049
1.1%
910000040
0.9%
900000053
1.2%

loan_amount
Real number (ℝ)

High correlation 

Distinct378
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15133450
Minimum300000
Maximum39500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2025-09-25T03:17:34.068665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile1800000
Q17700000
median14500000
Q321500000
95-th percentile30900000
Maximum39500000
Range39200000
Interquartile range (IQR)13800000

Descriptive statistics

Standard deviation9043363
Coefficient of variation (CV)0.59757443
Kurtosis-0.74367972
Mean15133450
Median Absolute Deviation (MAD)6900000
Skewness0.30872388
Sum6.46047 × 1010
Variance8.1782414 × 1013
MonotonicityNot monotonic
2025-09-25T03:17:34.230801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1060000027
 
0.6%
2000000024
 
0.6%
940000024
 
0.6%
2390000023
 
0.5%
1680000023
 
0.5%
1250000022
 
0.5%
1410000022
 
0.5%
1150000022
 
0.5%
860000021
 
0.5%
1200000021
 
0.5%
Other values (368)4040
94.6%
ValueCountFrequency (%)
3000006
 
0.1%
4000007
 
0.2%
50000016
0.4%
60000013
0.3%
70000015
0.4%
80000015
0.4%
90000012
0.3%
100000010
0.2%
110000018
0.4%
120000017
0.4%
ValueCountFrequency (%)
395000001
 
< 0.1%
388000001
 
< 0.1%
387000002
< 0.1%
385000001
 
< 0.1%
384000001
 
< 0.1%
382000003
0.1%
380000001
 
< 0.1%
379000002
< 0.1%
378000002
< 0.1%
377000001
 
< 0.1%

loan_term
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.900445
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2025-09-25T03:17:34.367628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q16
median10
Q316
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.7091873
Coefficient of variation (CV)0.52375726
Kurtosis-1.2208527
Mean10.900445
Median Absolute Deviation (MAD)4
Skewness0.036358907
Sum46534
Variance32.594819
MonotonicityNot monotonic
2025-09-25T03:17:34.464606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6490
11.5%
12456
10.7%
4447
10.5%
10436
10.2%
18422
9.9%
16412
9.7%
20411
9.6%
14405
9.5%
2404
9.5%
8386
9.0%
ValueCountFrequency (%)
2404
9.5%
4447
10.5%
6490
11.5%
8386
9.0%
10436
10.2%
12456
10.7%
14405
9.5%
16412
9.7%
18422
9.9%
20411
9.6%
ValueCountFrequency (%)
20411
9.6%
18422
9.9%
16412
9.7%
14405
9.5%
12456
10.7%
10436
10.2%
8386
9.0%
6490
11.5%
4447
10.5%
2404
9.5%

cibil_score
Real number (ℝ)

High correlation 

Distinct601
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean599.93605
Minimum300
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2025-09-25T03:17:34.594293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile330
Q1453
median600
Q3748
95-th percentile869
Maximum900
Range600
Interquartile range (IQR)295

Descriptive statistics

Standard deviation172.4304
Coefficient of variation (CV)0.28741463
Kurtosis-1.1856696
Mean599.93605
Median Absolute Deviation (MAD)147
Skewness-0.0090392773
Sum2561127
Variance29732.243
MonotonicityNot monotonic
2025-09-25T03:17:34.743761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34816
 
0.4%
54315
 
0.4%
53815
 
0.4%
41514
 
0.3%
43914
 
0.3%
77814
 
0.3%
81914
 
0.3%
50914
 
0.3%
83713
 
0.3%
86513
 
0.3%
Other values (591)4127
96.7%
ValueCountFrequency (%)
30011
0.3%
3018
0.2%
30213
0.3%
3036
0.1%
3048
0.2%
3053
 
0.1%
3069
0.2%
3078
0.2%
3086
0.1%
3096
0.1%
ValueCountFrequency (%)
9006
0.1%
8996
0.1%
8985
0.1%
8974
 
0.1%
89611
0.3%
89511
0.3%
8945
0.1%
8932
 
< 0.1%
8928
0.2%
8918
0.2%

residential_assets_value
Real number (ℝ)

High correlation  Zeros 

Distinct278
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7472616.5
Minimum-100000
Maximum29100000
Zeros45
Zeros (%)1.1%
Negative28
Negative (%)0.7%
Memory size33.5 KiB
2025-09-25T03:17:34.885574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-100000
5-th percentile300000
Q12200000
median5600000
Q311300000
95-th percentile21260000
Maximum29100000
Range29200000
Interquartile range (IQR)9100000

Descriptive statistics

Standard deviation6503636.6
Coefficient of variation (CV)0.87032923
Kurtosis0.18473799
Mean7472616.5
Median Absolute Deviation (MAD)4100000
Skewness0.9784506
Sum3.19006 × 1010
Variance4.2297289 × 1013
MonotonicityNot monotonic
2025-09-25T03:17:35.042184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40000066
 
1.5%
50000063
 
1.5%
10000060
 
1.4%
100000057
 
1.3%
60000056
 
1.3%
130000055
 
1.3%
30000052
 
1.2%
70000052
 
1.2%
320000050
 
1.2%
20000050
 
1.2%
Other values (268)3708
86.9%
ValueCountFrequency (%)
-10000028
0.7%
045
1.1%
10000060
1.4%
20000050
1.2%
30000052
1.2%
40000066
1.5%
50000063
1.5%
60000056
1.3%
70000052
1.2%
80000045
1.1%
ValueCountFrequency (%)
291000001
< 0.1%
287000001
< 0.1%
285000002
< 0.1%
284000001
< 0.1%
283000001
< 0.1%
282000002
< 0.1%
280000001
< 0.1%
276000002
< 0.1%
275000001
< 0.1%
274000001
< 0.1%

commercial_assets_value
Real number (ℝ)

High correlation  Zeros 

Distinct188
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4973155.3
Minimum0
Maximum19400000
Zeros107
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2025-09-25T03:17:35.194802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200000
Q11300000
median3700000
Q37600000
95-th percentile13900000
Maximum19400000
Range19400000
Interquartile range (IQR)6300000

Descriptive statistics

Standard deviation4388966.1
Coefficient of variation (CV)0.88253148
Kurtosis0.1008126
Mean4973155.3
Median Absolute Deviation (MAD)2800000
Skewness0.95779089
Sum2.12304 × 1010
Variance1.9263023 × 1013
MonotonicityNot monotonic
2025-09-25T03:17:35.369756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0107
 
2.5%
200000101
 
2.4%
100000100
 
2.3%
30000090
 
2.1%
50000083
 
1.9%
80000076
 
1.8%
70000074
 
1.7%
40000071
 
1.7%
60000070
 
1.6%
100000067
 
1.6%
Other values (178)3430
80.3%
ValueCountFrequency (%)
0107
2.5%
100000100
2.3%
200000101
2.4%
30000090
2.1%
40000071
1.7%
50000083
1.9%
60000070
1.6%
70000074
1.7%
80000076
1.8%
90000052
1.2%
ValueCountFrequency (%)
194000001
 
< 0.1%
190000003
0.1%
189000001
 
< 0.1%
188000002
< 0.1%
187000001
 
< 0.1%
185000004
0.1%
184000003
0.1%
183000001
 
< 0.1%
182000001
 
< 0.1%
179000003
0.1%

luxury_assets_value
Real number (ℝ)

High correlation 

Distinct379
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15126306
Minimum300000
Maximum39200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2025-09-25T03:17:35.523370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile1900000
Q17500000
median14600000
Q321700000
95-th percentile31300000
Maximum39200000
Range38900000
Interquartile range (IQR)14200000

Descriptive statistics

Standard deviation9103753.7
Coefficient of variation (CV)0.6018491
Kurtosis-0.73805613
Mean15126306
Median Absolute Deviation (MAD)7100000
Skewness0.3222075
Sum6.45742 × 1010
Variance8.2878331 × 1013
MonotonicityNot monotonic
2025-09-25T03:17:35.677580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
620000026
 
0.6%
290000026
 
0.6%
2040000026
 
0.6%
1200000024
 
0.6%
1490000024
 
0.6%
1230000024
 
0.6%
1720000023
 
0.5%
710000023
 
0.5%
990000022
 
0.5%
1390000022
 
0.5%
Other values (369)4029
94.4%
ValueCountFrequency (%)
3000004
 
0.1%
40000010
0.2%
50000014
0.3%
60000014
0.3%
70000014
0.3%
80000016
0.4%
90000013
0.3%
10000008
0.2%
110000019
0.4%
120000016
0.4%
ValueCountFrequency (%)
392000001
 
< 0.1%
391000001
 
< 0.1%
386000002
 
< 0.1%
382000004
0.1%
381000005
0.1%
380000001
 
< 0.1%
379000002
 
< 0.1%
378000002
 
< 0.1%
377000001
 
< 0.1%
376000002
 
< 0.1%

bank_asset_value
Real number (ℝ)

High correlation 

Distinct146
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4976692.4
Minimum0
Maximum14700000
Zeros8
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size33.5 KiB
2025-09-25T03:17:35.827934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile500000
Q12300000
median4600000
Q37100000
95-th percentile11100000
Maximum14700000
Range14700000
Interquartile range (IQR)4800000

Descriptive statistics

Standard deviation3250185.3
Coefficient of variation (CV)0.65308141
Kurtosis-0.39727743
Mean4976692.4
Median Absolute Deviation (MAD)2400000
Skewness0.56072501
Sum2.12455 × 1010
Variance1.0563705 × 1013
MonotonicityNot monotonic
2025-09-25T03:17:35.981969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
490000063
 
1.5%
140000063
 
1.5%
360000063
 
1.5%
450000061
 
1.4%
160000060
 
1.4%
540000059
 
1.4%
90000058
 
1.4%
130000057
 
1.3%
240000054
 
1.3%
410000054
 
1.3%
Other values (136)3677
86.1%
ValueCountFrequency (%)
08
 
0.2%
10000031
0.7%
20000054
1.3%
30000050
1.2%
40000050
1.2%
50000040
0.9%
60000050
1.2%
70000042
1.0%
80000039
0.9%
90000058
1.4%
ValueCountFrequency (%)
147000002
< 0.1%
146000002
< 0.1%
144000001
 
< 0.1%
143000001
 
< 0.1%
142000002
< 0.1%
141000003
0.1%
140000003
0.1%
139000004
0.1%
138000002
< 0.1%
137000001
 
< 0.1%

loan_status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size33.5 KiB
Approved
2656 
Rejected
1613 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters38421
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApproved
2nd rowRejected
3rd rowRejected
4th rowRejected
5th rowRejected

Common Values

ValueCountFrequency (%)
Approved2656
62.2%
Rejected1613
37.8%

Length

2025-09-25T03:17:36.113900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-25T03:17:36.182922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
approved2656
62.2%
rejected1613
37.8%

Most occurring characters

ValueCountFrequency (%)
e7495
19.5%
p5312
13.8%
d4269
11.1%
4269
11.1%
r2656
 
6.9%
A2656
 
6.9%
v2656
 
6.9%
o2656
 
6.9%
R1613
 
4.2%
j1613
 
4.2%
Other values (2)3226
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)38421
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e7495
19.5%
p5312
13.8%
d4269
11.1%
4269
11.1%
r2656
 
6.9%
A2656
 
6.9%
v2656
 
6.9%
o2656
 
6.9%
R1613
 
4.2%
j1613
 
4.2%
Other values (2)3226
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)38421
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e7495
19.5%
p5312
13.8%
d4269
11.1%
4269
11.1%
r2656
 
6.9%
A2656
 
6.9%
v2656
 
6.9%
o2656
 
6.9%
R1613
 
4.2%
j1613
 
4.2%
Other values (2)3226
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)38421
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e7495
19.5%
p5312
13.8%
d4269
11.1%
4269
11.1%
r2656
 
6.9%
A2656
 
6.9%
v2656
 
6.9%
o2656
 
6.9%
R1613
 
4.2%
j1613
 
4.2%
Other values (2)3226
8.4%

Interactions

2025-09-25T03:17:29.715097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:16.573462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:18.348154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:19.799545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:21.072338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:22.571739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:23.858132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:25.138317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:26.431556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:28.000728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-09-25T03:17:22.130414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:23.598464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:24.877577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:26.172924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:27.724942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:29.345024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:31.437987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:18.155105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:19.678127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:20.938518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:22.431868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:23.729808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:25.011853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:26.308541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:27.861725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-25T03:17:29.533302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-25T03:17:36.263113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
bank_asset_valuecibil_scorecommercial_assets_valueeducationincome_annumloan_amountloan_statusloan_termluxury_assets_valueno_of_dependentsresidential_assets_valueself_employedloan_id
bank_asset_value1.000-0.0200.5890.0000.8790.8400.0000.0140.8360.0130.5690.0000.015
cibil_score-0.0201.000-0.0010.000-0.023-0.0200.8750.008-0.030-0.010-0.0310.0000.016
commercial_assets_value0.589-0.0011.0000.0000.6430.6220.018-0.0010.6190.0040.4480.0280.021
education0.0000.0000.0001.0000.0000.0280.0000.0000.0140.0000.0000.0170.000
income_annum0.879-0.0230.6430.0001.0000.9410.0200.0110.9430.0070.6380.0350.013
loan_amount0.840-0.0200.6220.0280.9411.0000.0520.0120.894-0.0010.6130.0000.012
loan_status0.0000.8750.0180.0000.0200.0521.0000.1840.0400.0000.0160.0000.038
loan_term0.0140.008-0.0010.0000.0110.0120.1841.0000.008-0.0200.0030.0390.010
luxury_assets_value0.836-0.0300.6190.0140.9430.8940.0400.0081.0000.0050.6130.0000.001
no_of_dependents0.013-0.0100.0040.0000.007-0.0010.000-0.0200.0051.0000.0120.0240.006
residential_assets_value0.569-0.0310.4480.0000.6380.6130.0160.0030.6130.0121.0000.0000.026
self_employed0.0000.0000.0280.0170.0350.0000.0000.0390.0000.0240.0001.0000.012
loan_id0.0150.0160.0210.0000.0130.0120.0380.0100.0010.0060.0260.0121.000

Missing values

2025-09-25T03:17:31.707308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-25T03:17:31.922362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

loan_idno_of_dependentseducationself_employedincome_annumloan_amountloan_termcibil_scoreresidential_assets_valuecommercial_assets_valueluxury_assets_valuebank_asset_valueloan_status
012GraduateNo96000002990000012778240000017600000227000008000000Approved
120Not GraduateYes41000001220000084172700000220000088000003300000Rejected
233GraduateNo91000002970000020506710000045000003330000012800000Rejected
343GraduateNo8200000307000008467182000003300000233000007900000Rejected
455Not GraduateYes98000002420000020382124000008200000294000005000000Rejected
560GraduateYes4800000135000001031968000008300000137000005100000Rejected
675GraduateNo87000003300000046782250000014800000292000004300000Approved
782GraduateYes57000001500000020382132000005700000118000006000000Rejected
890GraduateYes80000022000002078213000008000002800000600000Approved
9105Not GraduateNo11000004300000103883200000140000033000001600000Rejected
loan_idno_of_dependentseducationself_employedincome_annumloan_amountloan_termcibil_scoreresidential_assets_valuecommercial_assets_valueluxury_assets_valuebank_asset_valueloan_status
425942600Not GraduateYes45000001150000014509134000002300000154000005900000Rejected
426042615GraduateNo880000029300000105601680000013900000311000009900000Approved
426142623GraduateYes3000000750000068811400000450000061000002300000Approved
426242635GraduateNo13000003000000205401000000230000032000001900000Rejected
426342643GraduateNo5000000127000001486547000008100000195000006300000Approved
426442655GraduateYes100000023000001231728000005000003300000800000Rejected
426542660Not GraduateYes3300000113000002055942000002900000110000001900000Approved
426642672Not GraduateNo65000002390000018457120000012400000181000007300000Rejected
426742681Not GraduateNo41000001280000087808200000700000141000005800000Approved
426842691GraduateNo9200000297000001060717800000118000003570000012000000Approved